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Insurance fraud detection: how AI analyzes claims calls

Insurance fraud accounts for 5-15% of total claims costs in the US. AI identifies in transcribed text the patterns that trained human ears miss in daily call volume.

Insurance fraud: a scale problem, not an intent problem

No insurer can afford to listen to 100% of adjuster calls. With hundreds or thousands of monthly claims, the only viable model was random sampling: review 5-10% of calls and hope fraudulent cases fall in the sample.

The result: most fraud goes undetected. Not because adjusters are negligent, but because it's physically impossible to catch it without reviewing every conversation.

AI changes this equation completely. It doesn't listen to calls — it analyzes transcribed text. And it can analyze 1,000 calls in the time a human would need to review 5.

What fraud patterns AI detects in claims transcripts

Narrative inconsistencies

A claimant who describes a collision as "rear-end" in one call and "side impact" in another is showing a factual inconsistency that may indicate fabrication. AI automatically compares claimant statements across multiple calls and alerts when contradictions appear.

Vocabulary associated with staged claims

Fraudulent claimants tend to use specific vocabulary: overly legal terminology for someone without legal training, references to specific settlement amounts before the claim has been evaluated, or an excessively detailed narrative with no gaps (genuine witnesses typically have more uncertainty).

Evasion under direct questioning

Automatic diarization allows analyzing not just what the claimant says, but how they respond to specific questions from the adjuster. Evasive answers, topic changes or unusually long pauses before direct questions are signals the system can identify.

Important: AI doesn't make denial decisions — it generates alerts that adjusters or the SIU (Special Investigations Unit) evaluate. The goal is to prioritize which cases warrant deeper investigation, not to automate the final decision.

5-15%
of claim costs attributed to fraud (US)
-70%
documentation time per claim file
100%
calls analyzed vs. 5-10% manually

Automatic claims file documentation: the most immediate benefit

Beyond fraud detection, the most immediate benefit of AI-powered insurance call transcription is claims documentation.

Each call with a policyholder automatically generates:

Adjusters no longer write post-call notes. The file updates automatically with each interaction.

State insurance regulations: documentation requirements

State insurance commissioners have specific requirements for claims documentation. Most states require insurers to maintain complete records of all communications with policyholders during claims processing. NAIC model regulations provide baseline standards that most states have adopted.

With automatic documentation, the audit trail for each claim file is complete: who called, when, what was said, what commitments were made. This facilitates regulatory examinations and helps the insurer identify processing bottlenecks.

Training new adjusters with real call examples

One of the least obvious but most valuable uses of transcripts is training. Well-handled and poorly-handled claims calls are the best training material for new adjusters.

With a library of transcribed and tagged calls, the training department can build a catalog of real cases illustrating exactly how to handle specific situations — the equivalent of medical case studies, but for claims handling.

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